CN102988041B - Signal-selectivity averaging method in cardiac magnetic signal noise suppression - Google Patents
Signal-selectivity averaging method in cardiac magnetic signal noise suppression Download PDFInfo
- Publication number
- CN102988041B CN102988041B CN201210465494.1A CN201210465494A CN102988041B CN 102988041 B CN102988041 B CN 102988041B CN 201210465494 A CN201210465494 A CN 201210465494A CN 102988041 B CN102988041 B CN 102988041B
- Authority
- CN
- China
- Prior art keywords
- signal
- mrow
- masterplate
- average
- msub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
A kind of signal-selectivity averaging method in suppressing the present invention relates to cardiac magnetic signal noise, it is characterized in that mode of the described method based on stencil matching, by calculating the similarity factor between masterplate signal and tested signal, judge that rejecting part interference cycle is averaged through threshold values, to realize optional signal averaging.The characteristics of this method is that the cycle that noise jamming or abnormal signal in signal be present is rejected using stencil matching, avoid introducing average signal result unnecessary noise priming signal distortion, flexibility ratio is big, suitable for the various interference problems run into during actual acquisition mcg-signalses.
Description
Technical field
Signal-selectivity averaging method in suppressing the present invention relates to a kind of cardiac magnetic signal noise, more precisely, this hair
It is bright to be related to a kind of signal-selectivity averaging method suppressed applied to cardiac magnetic signal noise.
Background technology
SPUID (Superconducting Quantum Interference Device, SQUID) is
Most sensitive magnetic flux transducer is currently known, representative cryogenic superconduction SQUID magnetic field sensitivity is 3-5fT/sqrt (Hz).As
A SQUID important applied field, is proved, heart magnetic system is in heart disease diagnosis, functional study etc. through clinical research
There is the application potential of uniqueness【V.Pizzela etal, Supercond.Sci.Technol.14 (2001) R79-R114】.
The signal that the collection of heart magnetic system obtains includes useful signal and noise jamming, and wherein noise includes white noise, non-white
Noise, external environmental interference, and some are abnormal etc. from signal change in itself, local signal.After pretreatment, heart magnetic
' stubbornness ' noise, the disturbance occurred such as pulse, at random and local significant signal are still had in result obtained by signal
Burr etc..In order to eliminate these interference noises, multicycle average treatment is widely used.
Averagely it is that to commonly use during processing of biomedical signals improve the most frequently used side of cycle or quasi-periodic signal quality
One of method, it is widely used in mcg-signalses processing.In mcg-signalses average treatment, by gathering synchronous electrocardiosignal,
Carry out blocking averagely on the basis of the R peaks of electrocardiosignal.At present, the most frequently used method mainly carries out average place to full section signal
Reason, this method cause these obstinate noises to be introduced into average result to the end, cause distorted signals.In addition also have and cluster
Analytic approach【DDiPietroPaolo, H-P Muller and S N Erne, Phys.Med.Biol.50 (2005) 2415-
2426】, it is that each signal segment is divided into tree structure according to signal characteristic, but due to excessively finely causing complexity
It is larger, it is less efficient.
Therefore, it is those skilled in the art's effort to seek high efficiency, strong practicality, easy-operating mcg-signalses averaging method
One of target of pursuit, while have in terms of practical application and analysis and be of great significance, it is built into the design of the present invention.
The content of the invention
A kind of signal-selectivity averaging method in suppressing it is an object of the invention to provide cardiac magnetic signal noise, is provided
Method be it is a kind of signal noise suppression in signal-selectivity averaging method, side of the described method based on stencil matching
Formula, by calculating the similarity factor between masterplate signal and tested signal, reject part interference cycle through threshold decision and put down
, to realize optional signal averaging.
Signal-selectivity averaging method of the present invention is：
（1）Reference electrocardiosignal R peaks corresponding with pending mcg-signalses are searched, on the basis of corresponding electrocardiosignal R peaks,
Mcg-signalses are divided into each independent cycle；Carry out the single-revolution wavelength-division section of heart magnetic；
（2）Pending signal is subjected to full average treatment, the average signal result of acquisition is as primary masterplate；
（3）The similarity factor between each original segment cycle signal and primary masterplate is calculated, is entered with the first threshold value of setting
Row compares, and identifies and rejects all wave bands that waveform changes greatly, all wave bands remained are averaged, obtain final masterplate
Signal；
Similarity factor γ represents similarity degree between the two, is defined as：
γ=1-δ
δ is difference coefficient, reflects current demand signal and shape difference of the masterplate signal on each time point：
Wherein, CkFor masterplate signal, XkTo be detected signal.During waveform recognition, similarity factor becomes to the amplitude of signal
Change and ripple is wide more sensitive, it is stronger for the recognition capability of waveform change.
Threshold size determines the degree that signal behavior is rejected, and directly affects the quality of masterplate signal, therefore select suitably
Threshold value is very crucial.Irrational threshold value may cause selection deficiency or cross the situation generation of selection.Selection is excessive, and this makes
Obtain the available cycle that signal retains to reduce, the SNR of acquired results is poor, can not embody coherence average and suppress noise, improving SNR
The advantage of aspect, relative to selection deficiency, the harm for crossing selection is bigger.In order to avoid there is selection phenomenon, using relative threshold
Value, is defined as：
Gate=μ × γmean, 0≤μ≤1
Wherein, γmeanFor the average of similarity factor sequence, μ is intensity factor, can be carried out according to the situation of real data
Selection.
（4）Repeat step（3）, stencil matching is with final masterplate instead of the final average mcg-signalses of primary masterplate acquisition.
In order to verify the availability of selective averaging method provided by the invention, Fig. 2 (a) gives one group of simulation MCG letter
Number and signal behavior result, by stencil matching, may recognize that in the presence of the cycle significantly interfered with.Fig. 2 (b) sets forth simulation
The full average result of heart magnetic, select average result and be removed the average result of cycle.It can be seen that full average result is in waveform
It is upper exist with the curve for being removed cycle average result it is certain associate, be the direct sources for causing wave distortion.Selectivity
The cycle that local interference be present can averagely be efficiently identified and rejected, so as to suppress the influence of interference noise.With reference
Signal is compared, and selection averagely can preferably keep signal waveform.
Selective averaging method is applied to handle real mcg-signalses.Fig. 3 (a) gives one group of true mcg-signals
And selection result.It can be seen that selective averaging method can effectively have the cycle of interference noise in identification signal.Fig. 3 (b)
The full average result of true mcg-signalses is sets forth, select average result and is removed the average result of part.Therefrom
As can be seen that being removed part has obvious interference, full average treatment result is influenceed, causes waveform local amplitude to decay, choosing
Selecting property average result then inhibits the influence that external interference is brought, and can more really reflect actual mcg-signalses feature.
Except local low frequency disturbs, selective averaging method is equally applicable for other types noise jamming, such as pulse, letter
Number burr, local anomaly etc..Fig. 4 give exist signal burr, impulse disturbances true mcg-signalses selection reject example.
In a word, it is that one kind can the invention discloses a kind of signal averaging method suppressed applied to cardiac magnetic signal noise
Effectively propose the average treatment method of interference cycle occurred in actual acquisition signal process.Mould is determined by preselected mode
Version signal, the similarity factor calculated between each independent heart magnetic cycle ripple and masterplate signal realizes stencil matching, through threshold decision
Qualified cycle is obtained, the final average signal result obtained closer to actual signal.The present invention comprises the following steps：
（1）Mcg-signalses are divided into each independent cycle on the basis of corresponding electrocardiosignal R peaks；（2）Whole wave carries out coherence average
Obtain primary masterplate；（3）Ultimate masterplate is obtained by preselected；（4）Stencil matching obtains average signal result.The spy of this method
Point is that the cycle that noise jamming or abnormal signal in signal be present is rejected using stencil matching, avoids introducing average signal result
Unnecessary noise priming signal distortion, flexibility ratio is big, suitable for the various interference run into during actual acquisition mcg-signalses
Problem.
Brief description of the drawings
Fig. 1 is the algorithm flow chart of selective averaging method；
Fig. 2 (a) is simulation mcg-signalses and its selects result；
Fig. 2 (b) is simulation mcg-signalses average result；
Fig. 3 (a) is the average example of selectivity of mcg-signalses；
Fig. 3 (b) is true mcg-signalses average result；
Fig. 4 is the average example of selectivity of mcg-signalses.
Embodiment
Signal-selectivity averaging method provided by the invention is further explained with reference to embodiment and accompanying drawing
State, but the present invention is limited only to absolutely not embodiment.
Embodiment 1
Fig. 1 is the algorithm flow chart of selective averaging method：Pending mcg-signalses are subjected to full average treatment first
Obtain primary masterplate signal；Secondly stencil matching is carried out using MCD methods, calculates the phase of masterplate signal and pending mcg-signalses
Like coefficient；The cycle that the threshold condition of setting will be met again carries out the average final masterplate of acquisition；Then by final masterplate with treating
Survey signal and carry out the qualified cycle of stencil matching acquisition using MCD methods；Finally the signal cycle selected is put down
, selective average signal is obtained.
Fig. 2 is the average example of simulation mcg-signalses selection：Small coil simulation heart magnetic is driven using electrocardiosignal, passes through collection
System acquisition simulates mcg-signalses.Fig. 2 (a) disturbs cycle identification for simulation mcg-signalses：Figure top half is the mould collected
Intend mcg-signalses；Figure the latter half is the electrocardiosignal of synchronous acquisition, is corresponded with mcg-signalses, while with solid ★ in figure
The signal cycle for meeting threshold condition after the screening of selective averaging method is labelled with, hollow ☆ is labelled with the signal being removed
Cycle；This method can effectively reject all wave bands that interference in mcg-signalses be present（As shown in dotted rectangle in figure）.Fig. 2
(b) it is the normalization average result of simulation mcg-signalses：It is directly complete average to include normalization electrocardio reference signal, normalization respectively
As a result, normalization selects average result and is removed the normalization average result of part, for the ease of comparing normalization heart magnetic
Signal and reference signal, for the offset of reference signal addition -0.2；Interior illustration deducts reference signal for normalization mcg-signalses
Difference curve.Each average mcg-signalses of normalization are compared with reference signal, it can be seen that pass through selective averaging method institute
Average signal result closer to reference signal, remain the genuine property of signal, and directly average result is due to Local Dry
The presence disturbed causes average signal distortion phenomenon occur in subband, and directly average result is with being removed the average knot in part
Fruit has closing property of certain shape.Therefore, local noise interference can cause average signal result distortion, and selective mean square rule can
Effectively to suppress this influence.
Fig. 3 is the average example of true mcg-signalses selection.Fig. 3 (a) is the multicycle mcg-signalses that truly measure and its same
Walk electrocardiosignal：Figure top half is true mcg-signalses, and the latter half is corresponding electrocardiosignal；Solid ★ is marked
The signal cycle retained after chosen property averaging method screening, hollow ☆ mark the signal week for the presence larger interference being removed
Ripple（In figure shown in dotted rectangle）.For real mcg-signalses, selective averaging method equally effectively can unsuccessfully go out
In the presence of all wave bands of interference.Fig. 3 (b) is the average signal result of true mcg-signalses：Respectively include direct average signal result,
Selective average signal result and it is removed part average result.It can be seen that directly average result is average with being removed part
As a result the degree of correlation is larger, is easily disturbed all wave actions and causes average signal that distortion occurs, and selective average result then suppresses
The influence of interference cycle, remain the genuine property of signal.
Fig. 4 is that the selectivity of other pink noises rejects example：Chosen property averaging method is marked by solid ★ in figure
The signal cycle retained after screening, hollow ☆ mark the signal cycle for the presence larger interference being removed.Top half is to deposit
Example is rejected in the selection of the mcg-signalses of signal burr, the latter half is that the selection for the mcg-signalses that impulse disturbances be present is rejected
Example.For noise jamming common in mcg-signalses, selective averaging method can effectively be identified, so as to obtain more
Stick on the average signal result of nearly actual signal.
Embodiment 2
Signal-selectivity averaging method of the present invention comprises the concrete steps that：
1st, the R peaks with reference to electrocardiosignal corresponding with pending mcg-signalses are searched, as the benchmark of signal subsection, will be treated
The mcg-signalses of processing are divided into each independent cycle, and signal whole story edges at two ends data segment is judged, cast out imperfect
Cycle.
2nd, coherence average, i.e. corresponding points superposed average are carried out to whole heart magnetic independence cycles, the average result of gained is made
For primary masterplate.
3rd, to each heart magnetic independence cycle respectively compared with primary masterplate, similarity factor is calculated, obtains one group of phase
Like coefficient sequence；Rational threshold value is set, similarity factor sequence is judged compared with threshold value, is unsatisfactory for the week of threshold condition
Ripple is judged to bad signal and cast out.
4th, the signal cycle progress coherence average retained will be entered after screening, using acquired results as ultimate masterplate.Calculate
Similarity factor between each independent cycle and ultimate masterplate, by threshold decision, qualified cycle is concerned with
It is average, that is, obtain final selective average result.
Claims (5)
1. the signal-selectivity averaging method in a kind of cardiac magnetic signal noise suppression, it is characterised in that described method is based on masterplate
The mode of matching, by calculating the similarity factor between masterplate signal and tested signal, judge that rejecting part disturbs week through threshold values
Ripple is averaged, to realize optional signal averaging；
Including：
(1) reference electrocardiosignal R peaks corresponding with pending mcg-signalses are searched, on the basis of corresponding electrocardiosignal R peaks, by the heart
Magnetic signal is divided into each independent cycle；Carry out the single-revolution wavelength-division section of heart magnetic；
(2) pending signal is subjected to full average treatment, the average signal result of acquisition is as primary masterplate；
(3) similarity factor between each original segment cycle signal and primary masterplate is calculated, is compared with the first threshold value of setting
Compared with identifying and reject all wave bands that waveform changes greatly, all wave bands remained are averaged, obtain final masterplate letter
Number；
(4) repeat step (3), stencil matching replace primary masterplate to obtain final average mcg-signalses with final masterplate.
2. the method as described in claim 1, it is characterised in that described similarity factor γ represents similarity degree between the two,
It is defined as：
γ=1- δ
δ is difference coefficient, reflects current demand signal and shape difference of the masterplate signal on each time point：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>&delta;</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>M</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>|</mo>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mi>k</mi>
</msub>
<msub>
<mi>C</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>n</mi>
</mrow>
</msub>
<mo>-</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mi>k</mi>
</msub>
<msub>
<mi>X</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<mo>|</mo>
</mrow>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<msubsup>
<mi>C</mi>
<mi>k</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<mi>&delta;</mi>
<mo>&le;</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula, CkFor masterplate signal, XkTo be detected signal.
3. the method as described in claim 2, it is characterised in that during waveform recognition, similarity factor becomes to the amplitude of signal
Change and ripple is wide more sensitive, it is stronger for the recognition capability of waveform change.
4. the method as described in claim 1, it is characterised in that the first threshold values size of setting determines the degree that signal behavior is rejected,
The quality of masterplate signal is directly affected, using relative threshold, is defined as：
Gate=μ × γmean, 0≤μ≤1
In formula, γmeanFor the average of similarity factor sequence, μ is intensity factor, is selected according to the situation of real data.
5. the method as described in claim 1, it is characterised in that it is i.e. corresponding that step (2) treats the full average treatment of process signal progress
Point superposed average.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210465494.1A CN102988041B (en) | 2012-11-16 | 2012-11-16 | Signal-selectivity averaging method in cardiac magnetic signal noise suppression |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210465494.1A CN102988041B (en) | 2012-11-16 | 2012-11-16 | Signal-selectivity averaging method in cardiac magnetic signal noise suppression |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102988041A CN102988041A (en) | 2013-03-27 |
| CN102988041B true CN102988041B (en) | 2018-04-06 |
Family
ID=47917500
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210465494.1A Active CN102988041B (en) | 2012-11-16 | 2012-11-16 | Signal-selectivity averaging method in cardiac magnetic signal noise suppression |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102988041B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107530016A (en) | 2015-04-20 | 2018-01-02 | 深圳市长桑技术有限公司 | A kind of physiology sign information acquisition methods and system |
| WO2016168979A1 (en) | 2015-04-20 | 2016-10-27 | 深圳市长桑技术有限公司 | Vital sign analysis method and system |
| CN107361760B (en) * | 2017-07-12 | 2020-06-12 | 中国科学院上海微系统与信息技术研究所 | Magnetocardiogram instrument diagnosis system and magnetocardiogram instrument using the same |
| CN107590508A (en) * | 2017-08-25 | 2018-01-16 | 漫迪医疗仪器（上海）有限公司 | A kind of mcg-signalses adaptive processing method and system |
| CN107550484B (en) * | 2017-09-28 | 2020-02-07 | 漫迪医疗仪器（上海）有限公司 | Magnetocardiogram signal quality evaluation method and system |
| CN113317793B (en) * | 2021-06-11 | 2023-02-17 | 宁波大学 | Magnetocardiogram high-frequency signal analysis method, storage medium, and electronic device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6405076B1 (en) * | 1995-09-20 | 2002-06-11 | Protocol Systems, Inc. | Artifact rejector for repetitive physiologic-event-signal data |
| CN1608581A (en) * | 2004-11-08 | 2005-04-27 | 中国科学院物理研究所 | Adaptive digital filtering method for magnetocardiographic noise suppression |
| CN101449973A (en) * | 2007-12-04 | 2009-06-10 | 深圳迈瑞生物医疗电子股份有限公司 | Judgment index generation method and device for cardiac interference signal identification |
| CN101828918A (en) * | 2010-05-12 | 2010-09-15 | 重庆大学 | Electrocardiosignal R peak detection method based on waveform characteristic matching |
| CN101856225A (en) * | 2010-06-30 | 2010-10-13 | 重庆大学 | Method for detecting R wave crest of electrocardiosignal |
| CN101919695A (en) * | 2010-08-06 | 2010-12-22 | 李楚雅 | Electrocardiosignal QRS complex detection method based on wavelet transform |
| CN102178522A (en) * | 2011-04-29 | 2011-09-14 | 华南理工大学 | Method for detecting and locating R wave in QRS (Quantum Resonance Spectrometer) waves of electrocardiographic signals of mother and fetus |
| CN102680080A (en) * | 2012-05-03 | 2012-09-19 | 中国科学技术大学 | Unsteady-state signal detection method based on improved self-adaptive morphological filtering |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6827695B2 (en) * | 2002-10-25 | 2004-12-07 | Revivant Corporation | Method of determining depth of compressions during cardio-pulmonary resuscitation |
-
2012
- 2012-11-16 CN CN201210465494.1A patent/CN102988041B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6405076B1 (en) * | 1995-09-20 | 2002-06-11 | Protocol Systems, Inc. | Artifact rejector for repetitive physiologic-event-signal data |
| CN1608581A (en) * | 2004-11-08 | 2005-04-27 | 中国科学院物理研究所 | Adaptive digital filtering method for magnetocardiographic noise suppression |
| CN101449973A (en) * | 2007-12-04 | 2009-06-10 | 深圳迈瑞生物医疗电子股份有限公司 | Judgment index generation method and device for cardiac interference signal identification |
| CN101828918A (en) * | 2010-05-12 | 2010-09-15 | 重庆大学 | Electrocardiosignal R peak detection method based on waveform characteristic matching |
| CN101856225A (en) * | 2010-06-30 | 2010-10-13 | 重庆大学 | Method for detecting R wave crest of electrocardiosignal |
| CN101919695A (en) * | 2010-08-06 | 2010-12-22 | 李楚雅 | Electrocardiosignal QRS complex detection method based on wavelet transform |
| CN102178522A (en) * | 2011-04-29 | 2011-09-14 | 华南理工大学 | Method for detecting and locating R wave in QRS (Quantum Resonance Spectrometer) waves of electrocardiographic signals of mother and fetus |
| CN102680080A (en) * | 2012-05-03 | 2012-09-19 | 中国科学技术大学 | Unsteady-state signal detection method based on improved self-adaptive morphological filtering |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102988041A (en) | 2013-03-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102988041B (en) | Signal-selectivity averaging method in cardiac magnetic signal noise suppression | |
| CN108714026B (en) | Fine-grained electrocardiosignal classification method based on deep convolutional neural network and online decision fusion | |
| CN106963369B (en) | Electroencephalogram relaxation degree identification method and device based on neural network model | |
| CN107822622A (en) | Electrocardiographic diagnosis method and system based on depth convolutional neural networks | |
| CN109828318B (en) | Magnetic resonance sounding signal noise filtering method based on variational modal decomposition | |
| CN108229382A (en) | Vibration signal characteristics extracting method, device, storage medium and computer equipment | |
| CN105997043B (en) | A kind of pulse frequency extracting method based on wrist wearable device | |
| CN101259016A (en) | Method for real time automatically detecting epileptic character wave | |
| CN106646637A (en) | Method for removing peak noise in nuclear magnetism signal | |
| CN104887222A (en) | Reversible electroencephalogram analysis method | |
| CN105147252A (en) | Heart disease recognition and assessment method | |
| Zhang et al. | Identification of blasting vibration and coal-rock fracturing microseismic signals | |
| CN112270255B (en) | Electroencephalogram signal identification method and device, electronic equipment and storage medium | |
| CN111027488A (en) | Signal classification method and device | |
| CN108801634A (en) | The method and its application of bearing fault characteristics frequency are extracted based on singular value decomposition and the frequency band entropy of optimization | |
| CN107870205A (en) | A kind of damage of composite materials detection detection method based on wavelet analysis | |
| CN111914679A (en) | Wireless audio processing equipment for scene artificial intelligence signals | |
| Milani et al. | Identification of normal and abnormal heart sounds by prominent peak analysis | |
| CN110208211A (en) | A kind of near infrared spectrum noise-reduction method for Detecting Pesticide | |
| CN114403897A (en) | Human body fatigue detection method and system based on electroencephalogram signals | |
| Azami et al. | Automatic signal segmentation based on singular spectrum analysis and imperialist competitive algorithm | |
| Rajan et al. | Wavelet based bank of correlators approach for phonocardiogram signal classification | |
| CN111025100A (en) | Transformer ultrahigh frequency partial discharge signal mode identification method and device | |
| CN115293219A (en) | Wavelet and kurtosis fused pulse signal denoising method | |
| CN105156901B (en) | A kind of optical fiber early warning system noise-reduction method and device based on wavelet analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20181008 Address after: Room 801, block B, 1455 Ping Cheng Road, Jiading District, Shanghai. Patentee after: Man Di medical instruments (Shanghai) Co., Ltd. Address before: 200050 No. 865, Changning Road, Shanghai, Changning District Patentee before: Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences |
|
| TR01 | Transfer of patent right |